Book Image

Creators of Intelligence

By : Dr. Alex Antic
Book Image

Creators of Intelligence

By: Dr. Alex Antic

Overview of this book

A Gartner prediction in 2018 led to numerous articles stating that "85% of AI and machine learning projects fail to deliver.” Although it's unclear whether a mass extinction event occurred for AI implementations at the end of 2022, the question remains: how can I ensure that my project delivers value and doesn't become a statistic? The demand for data scientists has only grown since 2015, when they were dubbed the new “rock stars” of business. But how can you become a data science rock star? As a new senior data leader, how can you build and manage a productive team? And what is the path to becoming a chief data officer? Creators of Intelligence is a collection of in-depth, one-on-one interviews where Dr. Alex Antic, a recognized data science leader, explores the answers to these questions and more with some of the world's leading data science leaders and CDOs. Interviews with: Cortnie Abercrombie, Edward Santow, Kshira Saagar, Charles Martin, Petar Veličković, Kathleen Maley, Kirk Borne, Nikolaj Van Omme, Jason Tamara Widjaja, Jon Whittle, Althea Davis, Igor Halperin, Christina Stathopoulos, Angshuman Ghosh, Maria Milosavljevic, Dr. Meri Rosich, Dat Tran, and Stephane Doyen.
Table of Contents (23 chapters)
1
Chapter 1: Introducing the Creators of Intelligence

Generative AI and ChatGPT

AA: What does ChatGPT and generative AI mean for AI and data?

JTW: The architecture of transformers is 1) is adaptable to multiple purposes, and 2) can be multi-modal. This marks a drastic move away from the era of single purpose models and early steps away from what everyday language calls “narrow” AI. This also unlocks multi-modal uses cases and I expect a sharp increase in applications that incorporate and integrate multiple data types (for example, text and images)

The way that running generative models like ChatGPT multiple times with the same input yields different results, together with the multi-purpose nature of such models, means that governance and testing will have to undergo a paradigm shift. Governance on the underlying models of ChatGPT is quite meaningless as it can be used to generate both pizza or poison. Governance will then have to shift to the point where the model is fully formed into a use case and /or to the point...